Compressed sensing-based near-field channel estimation method and apparatus for extra-large-scale massive multiple-input multiple-output (xl-mimo), device, and storage medium

Abstract

Provided are a compressed sensing-based near-field channel estimation method and apparatus for extra-large-scale massive multiple-input multiple-output (XL-MIMO), a device, and a storage medium. The method includes: obtaining a channel vector of a compressed sensing-based near-field channel on which estimation is to be performed; inputting the channel vector into a constructed channel estimation model, such that the channel estimation model converts the channel vector into a polar-domain channel vector; compressing the polar-domain channel vector into a signal vector; adding noise to the signal vector, and obtaining a received signal vector; inputting the received signal vector into a built-in learned approximate message passing (LAMP) algorithm layer, such that the LAMP algorithm layer performs iteration on the received signal vector and calculates an estimated polar-domain channel vector corresponding to the polar-domain channel vector; and converting the estimated polar-domain channel vector into an estimate of the compressed sensing-based near-field channel.

1 . A compressed sensing-based near-field channel estimation method for extra-large-scale massive multiple-input multiple-output (XL-MIMO), comprising:
 obtaining a channel vector of a compressed sensing-based near-field channel on which estimation is to be performed; and   inputting the channel vector into a channel estimation model constructed by a deep neural network, whereby the channel estimation model converts the channel vector into a polar-domain channel vector by a built-in sparse transformation matrix; compressing the polar-domain channel vector into a signal vector by a built-in sensing matrix; adding noise to the signal vector, and obtaining a received signal vector; inputting the received signal vector into a built-in learned approximate message passing (LAMP) algorithm layer, whereby the LAMP algorithm layer performs an iterative calculation on the received signal vector by a built-in soft-thresholding function, and obtaining an estimated polar-domain channel vector corresponding to the polar-domain channel vector, wherein the soft-thresholding function is calculated based on a built-in linear transformation parameter, nonlinear transformation parameter, and linear transformation matrix; and converting, by the built-in sparse transformation matrix, the estimated polar-domain channel vector into an estimate of the compressed sensing-based near-field channel on which estimation is to be performed.   
     
     
         2 . The compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 1 , further comprising: training the channel estimation model in two stages:
 adjusting, in a first stage, an initial sensing matrix and an initial sparse transformation matrix based on a first loss function until the first loss function converges; and   adjusting, in a second stage, an initial linear transformation parameter, an initial nonlinear transformation parameter, and an initial linear transformation matrix based on a second loss function until the second loss function converges.   
     
     
         3 . The compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 2 , wherein the adjusting, in a first stage, an initial sensing matrix and an initial sparse transformation matrix based on a first loss function until the first loss function converges comprises:
 obtaining a plurality of first channel vectors with a first true label, and initializing values of the sensing matrix, the sparse transformation matrix, the linear transformation parameter, the nonlinear transformation parameter, and the linear transformation matrix in the to-be-trained channel estimation model, wherein the first true label is used to represent a true estimate of each of the first channel vectors;   inputting the first channel vector into the to-be-trained channel estimation model;   converting, by the to-be-trained channel estimation model, the first channel vector into a first polar-domain channel vector through the initial sparse transformation matrix;   compressing the first polar-domain channel vector into a first signal vector by the initial sensing matrix;   adding first noise to the first signal vector, and obtaining a first received signal vector;   inputting the first received signal vector into the built-in LAMP algorithm layer to calculate a final estimated polar-domain channel vector of the LAMP algorithm layer based on the initial linear transformation parameter, the initial nonlinear transformation parameter, the initial linear transformation matrix, and the initial sensing matrix;   obtaining, based on the initial sparse transformation matrix and the final estimated polar-domain channel vector, a first estimate corresponding to the first channel vector;   calculating a value of the first loss function based on the first channel vector, the first estimate, and a formula of the first loss function; and   after calculating one value of the first loss function each time, determining whether the first loss function converges currently; and if the first loss function does not converge currently, adjusting the value of the sensing matrix and the value of the sparse transformation matrix, and continuously training the to-be-trained channel estimation model; or if the first loss function converges currently, determining that the training of the to-be-trained channel estimation model in the first stage has been completed, and obtaining an optimized sensing matrix and an optimized sparse transformation matrix.   
     
     
         4 . The compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 3 , wherein the LAMP algorithm layer contains a plurality of algorithm sublayers;
 for a first algorithm sublayer in the LAMP algorithm layer, a first soft-thresholding function of the first algorithm sublayer is calculated by the initial linear transformation parameter, the initial nonlinear transformation parameter, and the initial linear transformation matrix; and a first estimated polar-domain channel vector of the first algorithm sublayer is calculated based on the first soft-thresholding function and the first signal vector;   for a non-first algorithm sublayer in the LAMP algorithm layer, an estimated polar-domain channel vector of a current algorithm sublayer is calculated based on a current sensing matrix, an estimated polar-domain channel vector of a previous algorithm sublayer corresponding to the current algorithm sublayer, and a soft-thresholding function of the previous algorithm sublayer; and   the final estimated polar-domain channel vector of the LAMP algorithm layer is calculated based on estimated polar-domain channel vectors of all the algorithm sublayers in the LAMP algorithm layer.   
     
     
         5 . The compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 4 , wherein the adjusting, in a second stage, an initial linear transformation parameter, an initial nonlinear transformation parameter, and an initial linear transformation matrix based on a second loss function until the second loss function converges comprises:
 obtaining a plurality of second channel vectors with a second true label, wherein the second true label is used to represent a true estimate of each of the second channel vectors;   inputting the second channel vector into the channel estimation model in the second stage;   converting, by the to-be-trained channel estimation model, the second channel vector into a second polar-domain channel vector through the optimized sparse transformation matrix;   compressing the second polar-domain channel vector into a second signal vector by the optimized sensing matrix;   adding second noise to the second signal vector, and obtaining a second received signal vector;   inputting the second received signal vector into the built-in LAMP algorithm layer to calculate a second estimated polar-domain channel vector of each algorithm sublayer in the LAMP algorithm layer based on the initial linear transformation parameter, the initial nonlinear transformation parameter, the initial linear transformation matrix, and the optimized sensing matrix;   obtaining a second estimate of each algorithm sublayer based on the optimized sparse transformation matrix and the second estimated polar-domain channel vector of each algorithm sublayer;   after calculating a second estimate of an algorithm sublayer each time, calculating a value of a second loss function of the algorithm sublayer based on the second channel vector, the second estimate, and a formula of the second loss function; and   after calculating one value of the second loss function each time, determining whether a second loss function of a current algorithm sublayer converges; and if the second loss function of the current algorithm sublayer does not converge, fixing a linear transformation parameter, a nonlinear transformation parameter, and a linear transformation matrix of a previous algorithm sublayer corresponding to the current algorithm sublayer, adjusting a linear transformation parameter, a nonlinear transformation parameter, and a linear transformation matrix of the current algorithm sublayer, and continuously training the to-be-trained channel estimation model; or if the second loss function of the current algorithm sublayer converges, determining that the training of the to-be-trained channel estimation model in the second stage has been completed, and obtaining an optimized linear transformation parameter, an optimized nonlinear transformation parameter, and an optimized linear transformation matrix.   
     
     
         6 . The compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 5 , wherein the inputting the second received signal vector into the built-in LAMP algorithm layer to calculate a second estimated polar-domain channel vector of each algorithm sublayer in the LAMP algorithm layer based on the initial linear transformation parameter, the initial nonlinear transformation parameter, the initial linear transformation matrix, and the optimized sensing matrix comprises:
 for the first algorithm sublayer in the LAMP algorithm layer, calculating a second estimated polar-domain channel vector of the first algorithm sublayer by the initial linear transformation parameter, the initial nonlinear transformation parameter, and the initial linear transformation matrix; and   for the non-first algorithm sublayer in the LAMP algorithm layer, fixing values of linear transformation parameters, nonlinear transformation parameters, and linear transformation matrices of all algorithm sublayers before the current algorithm sublayer, calculating a linear transformation parameter, a nonlinear transformation parameter, and a linear transformation matrix of the current algorithm sublayer based on a linear transformation parameter, a nonlinear transformation parameter, and a linear transformation matrix of the previous algorithm sublayer corresponding to the current algorithm sublayer, and calculating the second estimated polar-domain channel vector of the current algorithm sublayer based on the linear transformation parameter, the nonlinear transformation parameter, and the linear transformation matrix of the current algorithm sublayer.   
     
     
         7 . The compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 6 , further comprising:
 based on the optimized sparse transformation matrix, the optimized sensing matrix, the optimized linear transformation parameter, the optimized nonlinear transformation parameter, the optimized linear transformation matrix, and the estimate of the compressed sensing-based near-field channel on which estimation is to be performed, reconstructing the compressed sensing-based near-field channel on which estimation is to be performed.   
     
     
         8 . A compressed sensing-based near-field channel estimation apparatus for XL-MIMO, comprising:
 a channel vector obtaining module and an estimate calculation module, wherein   the channel vector obtaining module is configured to obtain a channel vector of a compressed sensing-based near-field channel on which estimation is to be performed; and   the estimate calculation module is configured to input the channel vector into a channel estimation model constructed by a deep neural network, whereby the channel estimation model converts the channel vector into a polar-domain channel vector by a built-in sparse transformation matrix; compress the polar-domain channel vector into a signal vector by a built-in sensing matrix; add noise to the signal vector, and obtain a received signal vector; input the received signal vector into a built-in LAMP algorithm layer, whereby the LAMP algorithm layer performs an iterative calculation on the received signal vector by a built-in soft-thresholding function, and obtain an estimated polar-domain channel vector corresponding to the polar-domain channel vector, wherein the soft-thresholding function is calculated based on a built-in linear transformation parameter, nonlinear transformation parameter, and linear transformation matrix; and convert, by the built-in sparse transformation matrix, the estimated polar-domain channel vector into an estimate of the compressed sensing-based near-field channel on which estimation is to be performed.   
     
     
         9 . A terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 1 . 
     
     
         10 . A non-transitory storage medium, wherein the non-transitory storage medium comprises a stored computer program, and the computer program is run to control a device on which the non-transitory storage medium is located to perform the compressed sensing-based near-field channel estimation method for XL-MIMO according to  claim 1 .